What question did this study set out to answer?

To create a machine learning model that can predict major complications following postmastectomy breast reconstruction.

April 17, 2026Open Access

Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications

Key Points

To create a machine learning model that can predict major complications following postmastectomy breast reconstruction.
Developed an internally validated machine learning model
Trained the model using structured and unstructured clinical data
Focus on predicting 1-year major complications
Successfully predicted major complications using the model
Provides groundwork for personalized risk assessment and decision support tools

Abstract

In this prognostic study of PMBR outcomes, an internally validated ML model trained on both structured and unstructured clinical data was used to predict 1-year major complications. Such models support personalized risk assessment, inform decision-making, and provide a foundation for future externally validated and prospectively tested decision-support tools.

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Authors

Mohammed S. Shaheen

University of Michigan

Brennen T. McManus

University of Michigan

Clara M. Cullen

Georgetown University

Journals

JAMA Network Open

Actions

Institutions

Stanford University

University of Michigan

Georgetown University

References and Citations

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Building similarity graph...

Analyzing shared references across papers

Machine Learning Model to Predict Postmastectomy Breast Reconstruction Complications

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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